Take 10 with... William Schierding
Dr William Schierding explains the role computational science in research, and the additional pieces of information he can dig out of data sets.
1. Describe your research topic to us in 10 words or less.
Finding regulatory genomic elements that impact disease.
2. Now describe it in everyday terms!
Scientists have had access to the whole genome sequence for about 20 years and it has led to a lot of great understanding about what pieces of our genome do. But only 1% of the genome is coding, so what does the non-coding genome do? Turns out it has a big part to play in regulation and disease, and lots of small hits to the regulatory elements can add up to a disease like Parkinson’s. My research is trying to work out which regions of the genome are tied to which genes, how these small hits add up to a much bigger effect, and what the underlying biology to that is.
3. What are some of the day-to-day research activities you carry out?
My day to day work is about computationally building layers of different information together so we can understand which piece of the genome is open and accessible – ie – whether it has the potential to be an enhancer (turning on genes) or a suppressor (turning off genes). This information can also tell us if a piece of DNA has a physical connection to a distant piece of DNA, and whether the physical connection is to a gene, which tells us what the associations and trends are for particular diseases.
4. What do you enjoy most about your research?
I like the final step after we’ve done all the layering and there is suddenly a meaningful outcome - a common finding between all the different layers - that seems to pull all the disease strings together.
5. Tell us something that has surprised or amused you in the course of your research.
I have been putting these spatial elements together to understand how they impact disease since 2014, yet we seem to be one of the only groups in the world doing this. I think it’s partly because of a lack of computational support, and partly because the pursuit of big data generation has caused a trailing effect to do the bioinformatics to support it. Computational scientists like me are the ones who go back and layer the data together and find some really cool things that were missed first time. This also makes my research a bit more Covid-proof because it doesn’t require the generation of new data or clinical trials.
6. How have you approached any challenges you’ve faced in your research?
The biggest challenge we have is convincing funders that we are doing something that is worth funding. The way we tackle this is by building collaborations across multiple different research groups that cover a multitude of phenotypes. Some scientists have a single focus on a single disease for their whole career, but computational scientists use their expertise to help those scientists realise what is in their data – which means we work on many different diseases all the time.
7. What questions have emerged as a result?
A few years ago the Michael J Fox Foundation put out a funding call asking if any researchers had novel methods to understand a specific gene that had some anecdotal evidence for associations to Parkinson’s disease. It seemed to be made for our research group. A few months later after some heavy grant writing, we found out we had been successful -- the MJFF did indeed find our ideas unique and worth exploring. Before that funding call, I hadn’t particularly considered Parkinson’s disease. In fact, all of my work at the Liggins Institute had been at the other end of life – genetic variation and its impact on development and/or birth characteristics. The MJFF grant has opened up doors for research and collaborations that we wouldn’t have otherwise been able to achieve, something that has changed the trajectory of the group in a very positive manner.
8. What impact is your research having or what impact do you hope it will have?
Looking at the regulatory interactions between genes, we are starting to be able to detect meaningful ways to stratify individuals. By that I mean taking two individuals who have a disease and saying this person is going to get the disease a little earlier than the other. It’s just an association for now but we think we can start using our information to pick apart why people get sick with particular diseases, and then separate them into who is going to get more sick and why.
9. If you collaborate across the University, or outside the University, who do you work with and how does it benefit your research?
We collaborate with researchers in Australia for our work on neurological diseases like Parkinson’s, and with a group in London for our research into the genetics of some early growth phenotypes. Both of these collaborations have already led to major papers. We are also looking at the ’Growing up in New Zealand’ and the ‘Growing up in Australia’ databases to explore different aspects of early growth genetics, for example asthma and the onset of autoimmune diseases. Each collaboration brings a different disease type for us to find a secondary outcome from the data.
10. What one piece of advice would you give your younger, less experienced research self?
Get an understanding of how the analysis and the funding works early on. At the Liggins we focus most of our time on research and research outputs (rather than teaching), which is a different approach to other parts of the university. It’s therefore really important for early career researchers to understand how to take advantage of opportunities and to build a research portfolio and proper collaborations early on. It took me a couple of years to build a major collaboration which means I am going to be four or five years out of my PhD before those collaborations really start finding funding.
I think a lot of researchers would love to have more computational expertise. Most people I talk to appreciate having more eyes looking at their data and coming up with higher impact publications or even additional publications that they hadn’t even thought of. If I had reached out sooner they would have been happy to collaborate, but it’s taken longer than it should have because I was new and afraid of pushing my way in. Having that first conversation can be the hardest but after that things seem to naturally progress very quickly. One of my few regrets is not doing that sooner.